There are times when the solution to a design problem may “instinctively” pop into your head. Yet, in some scenarios, your instinct might be a valid method in solving design problems.
But, one subjective decision on whether the CTA must be bigger or not, can cost you drop in conversion on the line and ultimately, millions.
Moreover, it’s pretty hard to convince people or your stakeholders when your designs are solely based on intuition. Because they always go in trusting your work with the blindfold half-on and are prepared to point to quantitative data backing-up your hypothesis.
Quantitative user research gives you numbers that you can rely on. Take an example of an online supermarket platform where a lot of decisions are taken w.r.t. the color scheme, UI elements, categorization of products, place of variants option, etc. Quantitative research helps you measure the success and failure of such design aesthetics; you just have to do the leg work.
Take a look at some of the quantitative user research methods and how they can be implemented in your research process.
There is always some disagreement among the designers over the placement of small UI elements. Let your user’s choice be the deciding factor here. Build different variations or versions of your web components or page and present the same to your users. This method will assist you in determining which CTA button is being preferred by your users, or which typography is more appealing to them, and alike.
Their engagement with each experience can be measured and collected in an analytics dashboard and analyzed through a statistical engine with the prior versions. Through comparative testing you can learn which version attracts more clicks or to demonstrate the positive/negative impact of new features or changes to a user experience.
A word of caution: there should only be one difference in all the variants. It will be easy to draw consensus this way and give you definite reasons for selecting one design over the other.
Card sorting is used for structuring your information architecture. It is a cost-effective method as you can provide users with labelled index cards or online card-sorting platforms when conducting remotely. Let them arrange it according to their understanding of topic and ask for debrief later on. Such sessions help participants to do the labelling and categorization as they like.
In an e-commerce website, you can see if under “Packed food items” users are searching for both instant and frozen food items or expecting them in separate categories. Even when you ask them to come up with their own categorization, you can always know how your users group information – are they thinking of nomenclature like “Gourmet Cravings” or looking for simple names like “Food and Beverages”.
Once you have all the data, look for common items, category names or themes, and for groups that were frequently paired together. It helps you organize content so that it suits your users’ mental models, rather than the point of view of your company.
It is a task based analysis wherein you ask users to look for individual elements from the navigation structure that you have built. Tree tests help in improving your navigation by observing how real users navigate your site rather than just going on assumption.
Continuing the e-commerce example, let’s assume you ask the participants to locate “instant noodles”. Your users may go in searching for it under “packed food” category instead of scrolling down to find the “instant food” category. This is how it is often conducted to design page layouts and navigation menus.
It’s the reverse of card sorting. Instead of creating own categories, tree testing asks users to work with an existing set of categories, and highlight where they believe an item is most likely to be located.
Post release, you can use Google Analytics (GA) to see the type of daily users visiting your website. You can compare it with the persona(s) that you have built. You can see where your users are dropping off and what activities they are performing. You can leverage insights on user trends, page with the highest interaction, and how are they navigating.
For instance, in the e-commerce venture, analytics will help you understand your user demographics, type of device they are using, product preferences, how far are they scrolling, et al.
GA makes data visualization easier. There are multiple options to represent your data like line graphs, pie charts and bar graphs, thus making it easy to explain it to all the stakeholders involved. Thus, with the information in capable hands, GA can do wonders for your business.
To achieve your ultimate goal of intuitive and easy-to-use interface, backing your design decisions with data from quantitative research will help you build experiences that are fit and relevant for your target audience in the long run.
Hence, instead of depending solely on subjectivity or qualitative research, design must be validated with quantitative research, and proven design practices, as design exists to solve real problems, which inherently makes it objective.
Your product’s success ultimately boils down to how well you have done the user research. As a UX studio our team is equipped to tackle any UX challenge thrown their way. For any assistance related to user research and analysis, feel free to talk to us.